Analysis of Dark AI Patterns Manipulation Vulnerability Levels in the E-Commerce Environment - Damian Kocot (Autor); Monika Bednarczyk (Autor); AGNIESZKA KNAP-STEFANIUK (Autor); Aneta Montano (Autor); Izabela Gontarek (Autor); Magdalena Maciaszczyk (Autor); Marta Pietrzykowska (Autor) (2026)
10.35808/ersj/4317
https://ersj.eu/journal/4317
Oryginalny artykuł naukowy
Purpose: The study aims to examine the levels of consumer susceptibility to Dark AI Patterns in the e-commerce environment and to identify the psychological and behavioral factors that shape this vulnerability. Design/Methodology/Approach: The research was conducted using an online survey carried out in 2025 on a sample of 429 respondents, measuring their responses to algorithmic pressure techniques such as scarcity cues, countdown timers, social proof, and AI-driven recommendations. Findings: The results indicate a moderate and relatively uniform susceptibility to Dark AI Patterns, with higher personalization and trust in AI increasing vulnerability, while greater algorithmic awareness plays a protective role. Practical Implications: The study highlights the need for responsible design of AI-enhanced interfaces, emphasizing transparency, limitation of manipulative cues, and support for users’ informed decision-making. Originality/Value: The article provides one of the first empirical assessments of consumer susceptibility to AI-driven manipulative design, offering insights relevant for researchers, practitioners, and regulators shaping the future of ethical e-commerce.
Keywords: nauki o polityce i administracji